{"as_of":"2026-08-16T13:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7ed2795e825d0ad02ea94a58b31bff9c5daf628abf814f500460eb9a5cbc72e2","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:55:03.169562Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.08005/citation-record","integrity":"/paper/1908.08005/integrity","json":"/paper/1908.08005/citation-record.json","paper":"/paper/1908.08005"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.736532Z","title":"Irrelevant Features and the Subset Selection Problem,","venue":null,"work_id":"225e68c6-4081-4cd9-b2be-59240410da3f","year":1994},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.009400Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:d40af001943ebc1496838577dea3634c13171bd09fca38ccf860c796f9c1f155","observation_id":"7e461ac8-7a8a-4810-a135-5514d1ee5084","resolution":{"observed_at":"2026-08-14T12:55:03.741456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00069","last_updated":"2019-02-03T21:06:50Z","snapshot_observed_at":"2026-08-14T19:08:48.754949Z","submitted_at":"2018-05-31T19:48:00Z","title":"Explaining Explanations: An Overview of Interpretability of Machine Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00069","snapshot_observed_at":"2026-08-14T12:55:03.015018Z","title":"Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.015018Z"},"links":{"cited_paper":"/paper/1806.00069","citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:ea40e05a46baf6edf1036c5ba13ae66d4c76adca51e5047b5b07a9b625960fc7","observation_id":"0ef8eac5-fc94-49d2-b9ec-1b4640399501","resolution":{"observed_at":"2026-08-14T12:55:03.015018Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.020857Z","title":null,"venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.020857Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:01f5e054b3809fb01b3326a9a4c6a39c908c65437f1c9170d2b8ed2a389fa291","observation_id":"0eb77eab-0329-4725-94ed-795dd3b2ee7d","resolution":{"observed_at":"2026-08-14T12:55:03.020857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1305.0445","last_updated":"2013-06-07T02:35:21Z","snapshot_observed_at":"2026-08-15T00:19:10.179185Z","submitted_at":"2013-05-02T14:33:28Z","title":"Deep Learning of Representations: Looking Forward","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1305.0445","snapshot_observed_at":"2026-08-14T12:55:03.025882Z","title":"Deep learning of representations: Looking forward,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.025882Z"},"links":{"cited_paper":"/paper/1305.0445","citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:4afe648457aabf9c9ad0ca6155ef7c74bfd8e703c14a010372e25b0735c14529","observation_id":"ae8614d8-e6b7-4e7b-9114-e7c190585c36","resolution":{"observed_at":"2026-08-14T12:55:03.025882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.710353Z","title":"Feature Construction Methods: A Survey,","venue":null,"work_id":"3f9e84bf-0b1c-4536-9747-cc964b27bb08","year":2009},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.031221Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:268a146b3921ec74290a7984e2cfb00b8cc86cb0afb9c778f99977faa34ee191","observation_id":"3f51d981-774e-4109-8c00-24f549d3bd8f","resolution":{"observed_at":"2026-08-14T12:55:03.715411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.695080Z","title":"Feature generation using general constructor functions,","venue":null,"work_id":"769c8dc9-f991-4496-8f39-7086352c5a60","year":2002},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.036053Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:b3b488ad0ee5864144901a3d850cd710cd3a4938a9ee75c99a5ef76cb62bb37f","observation_id":"af9aef26-85fe-4de9-83cc-ebc2331faace","resolution":{"observed_at":"2026-08-14T12:55:03.700261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.679323Z","title":"Cognito: Automated feature engineering for supervised learning,","venue":null,"work_id":"c0f9c8db-8fb8-41dc-b036-ca8316677563","year":2016},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.041294Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:eaceeec396be0c7619f83210c2b037147d3a0df9777553bbbec188b2773b9d73","observation_id":"172d1f00-2ece-41c4-a9a1-858ce28b802e","resolution":{"observed_at":"2026-08-14T12:55:03.684673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.07150","last_updated":"2017-09-21T04:04:43Z","snapshot_observed_at":"2026-08-14T20:31:10.996556Z","submitted_at":"2017-09-21T04:04:43Z","title":"Feature Engineering for Predictive Modeling using Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.07150","snapshot_observed_at":"2026-08-14T12:55:03.045860Z","title":"Feature engineering for predictive modeling using reinforcement learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.045860Z"},"links":{"cited_paper":"/paper/1709.07150","citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:28c9f005bbed372a1bd235e6d094254ad61198f1b08416c816c79e4c9b86cd6c","observation_id":"9e0a6314-6355-45a5-abcc-87213dafe969","resolution":{"observed_at":"2026-08-14T12:55:03.045860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.663070Z","title":"PSO for feature construction and binary classification","venue":null,"work_id":"c3bfb0cb-fa9d-4fc6-9f31-e350108458d1","year":2013},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.050716Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:6acb28ab3b0000b667c76222dac111fdedd003c3c61229329228128ccb5f6e9b","observation_id":"2c47829d-1224-4e0d-a0eb-d4a701ec3ba1","resolution":{"observed_at":"2026-08-14T12:55:03.667954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.646158Z","title":"New Representations in PSO for Feature Construction in Classification,","venue":null,"work_id":"c53bfb6c-2872-451f-a7cc-171ab1169f7e","year":2014},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.055308Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:c1d65aa36f8fa19671103b229214d67613b2af6a584a42eb3b43de1e3c29c42e","observation_id":"b7b867ce-6e99-45ce-8a65-72d2f9c4cce5","resolution":{"observed_at":"2026-08-14T12:55:03.651644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.631307Z","title":"A Survey on the Application of Genetic Programming to Classification,","venue":null,"work_id":"09149fcd-11a6-4fc5-a567-f0429b0b2132","year":2010},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.059818Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:cc0a5ca70bc9243cea45fd23a72df3e5bfd294e22e33da6fa4a30c37ed5818ba","observation_id":"fdb1d3be-971e-4133-936c-cc1c6b1d5d17","resolution":{"observed_at":"2026-08-14T12:55:03.636399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.616181Z","title":null,"venue":null,"work_id":"87383c1a-6b65-4e83-9ad2-4c07215085c4","year":1992},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.064245Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:b8cfdfa269aae1aee9380065f3cbc9502b073d6ac851d629bdb0f367fe3f3090","observation_id":"e29e8ffa-8896-4fe8-81bc-12009e1b9b8e","resolution":{"observed_at":"2026-08-14T12:55:03.620575Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.600647Z","title":"Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks,","venue":null,"work_id":"b67c3abd-72c6-499d-9d41-3ca3d56eb844","year":2002},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.068650Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:ce279874afff85e5a0a5833ff013215100d974ae16965aab2cc2aa8c0cdb79c4","observation_id":"7884f7c7-fa32-4a35-b376-37f121517c56","resolution":{"observed_at":"2026-08-14T12:55:03.605636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.586061Z","title":"Genetic Programming for Attribute Construction in Data Mining,","venue":null,"work_id":"9e6df7a1-ca77-471f-adbe-1d7a437dddad","year":2003},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.073387Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:d32aee77dc23fb5d279a48f93f584f967e80402f1d04471a742ddc708369ac0c","observation_id":"e32dadaa-1df2-4d44-a73b-a3e6ff5abf15","resolution":{"observed_at":"2026-08-14T12:55:03.590626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.570959Z","title":"Genetic Programming with a Genetic Al- gorithm for Feature Construction and Selection,","venue":null,"work_id":"5267d199-abd6-4312-a5bd-7c5077f17f51","year":2005},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.077771Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:3d488b03a74bac6f2f7df09cc791d094d55f6eec959589039ab99bbdc2331b01","observation_id":"26e3a550-9457-490d-a9a1-b4bdca60d2fb","resolution":{"observed_at":"2026-08-14T12:55:03.576157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.555065Z","title":"Genetic Programming and Class-Wise Orthogonal Transformation for Dimension Reduction in Classification Problems,","venue":null,"work_id":"54c7d634-2c52-4bfe-bcc0-786e006acacd","year":2008},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.082329Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:d06aa82f1c423df78991a28ae28ff48cd6a37ffcb303d181e2fb01359c687c36","observation_id":"52ed3bfe-a14d-4bfc-84ac-330959801a00","resolution":{"observed_at":"2026-08-14T12:55:03.560200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.539575Z","title":"Feature generation using genetic programming with application to fault classification,","venue":null,"work_id":"d1fecba7-82fa-4e9f-9e27-b6b226ac866f","year":2005},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.086851Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:89ff2cea8a963c48c14ee02d188bcb05ff949ee45fdef6784ec92067e74e62e0","observation_id":"79ede531-72d4-4ebb-82d0-e5b4eba84701","resolution":{"observed_at":"2026-08-14T12:55:03.544719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.524119Z","title":"A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming,","venue":null,"work_id":"05be3f8c-013c-4e6d-827b-444ac30d70a1","year":2012},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.091495Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:cefb77a3b7b4f9a075d02ee5c191f7da1bb6a31887cd56f487c496e10aababed","observation_id":"6ee22e0f-dd18-42e8-8f9d-5be3a2f79e36","resolution":{"observed_at":"2026-08-14T12:55:03.529567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.508942Z","title":"On Prediction of Epileptic Seizures by Computing Multiple Genetic Programming Artificial Fea- tures,","venue":null,"work_id":"7c996f8f-118a-4c8d-8100-111ea1748835","year":2005},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.095987Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:830eb1c3cce26d0f6e3f5d8573840bcffc0f2d9df5893421110810284f34c107","observation_id":"5cfe93a5-8094-491f-b6dd-85563e533c57","resolution":{"observed_at":"2026-08-14T12:55:03.513950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.494084Z","title":"Multiple feature construction in classification on high-dimensional data using GP","venue":null,"work_id":"274cb4d5-f101-4fe3-8bca-91f5a0e4de88","year":2016},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.100522Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:3100c927c6f59b75750a71cda285aafbaa54ee73f4675e610351cce24c0cac72","observation_id":"b47708e7-4022-4f09-ab8b-3d96c75c1f00","resolution":{"observed_at":"2026-08-14T12:55:03.498943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.478969Z","title":"Selecting and constructing features using grammatical evolution,","venue":null,"work_id":"b5a79d56-1efc-4202-a40c-d15bdd4a1703","year":2008},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.104913Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:6c623dede1b6d31c88191a5c1fa3f87c2965c04c1049e6788a270c7639b7effa","observation_id":"17306115-f456-4e9b-ad2e-c95b98c8bab4","resolution":{"observed_at":"2026-08-14T12:55:03.484103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.463816Z","title":"Strongly Typed Genetic Programming,","venue":null,"work_id":"f32fa28c-2566-4511-94ea-66b293d46161","year":1995},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.109678Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:2173170897b4465ef597b72c5524cbebb30baff3dcb29f9ce1e8418e76251638","observation_id":"df9093c9-8aa7-46d3-bd1a-4c23fd1bc65e","resolution":{"observed_at":"2026-08-14T12:55:03.468647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.449144Z","title":"Type Inheri- tance in Strongly Typed Genetic Programming,","venue":null,"work_id":"11976e80-31cc-46aa-8b07-fbe8cec4e3bb","year":1996},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.114444Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:dc561ffe513444c58cdb6033a8d0493f76f432386ffdbd795ee85d3cfcf2c86e","observation_id":"767ba587-cffa-4786-b623-791fba264a8a","resolution":{"observed_at":"2026-08-14T12:55:03.454011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.433715Z","title":"Grammar- based Genetic Programming: a survey,","venue":null,"work_id":"dde5372c-6541-42fb-8217-a4227c606678","year":2010},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.118668Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:58bd6247180959139843abeff3c1ad6cfb54ad669fb3891f888e57458eab7331","observation_id":"f068d33d-e28d-40cf-8e23-1d9d0c0a8f04","resolution":{"observed_at":"2026-08-14T12:55:03.438466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.418383Z","title":"Advances in Genetic Programming,","venue":null,"work_id":"f40d78ff-dd28-4956-9300-65b87bb0935f","year":1996},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.123236Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:be12c04bcd92af851184df97650f8cbe5396c88cd6ded9449321457eb91cacd4","observation_id":"f11ddaa4-5395-4d2b-983d-6d0133996b4c","resolution":{"observed_at":"2026-08-14T12:55:03.423215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.403055Z","title":"Dimensionally Aware Genetic Program- ming,","venue":null,"work_id":"6bb6b04f-6d14-4d7c-ac5b-2cd0b614ce66","year":1999},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.128030Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:227a699f670aae048996537dad0a3fe0ce1f6284ba1d0adb5dfafb7ff90fa8db","observation_id":"fcf446d4-c3d3-4b6b-95b4-f8e95ff69a38","resolution":{"observed_at":"2026-08-14T12:55:03.408141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.387685Z","title":"Grammar-guided genetic programming and dimensional consistency: application to non-parametric identification in mechanics,","venue":null,"work_id":"0144c68c-0832-4d2e-b9cd-deac4a5b2b10","year":2001},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.132731Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:425c47422991d20c14964246bb2df85c4260674d65189c27bd9385173bc3e5ee","observation_id":"c84294c7-d0be-485d-bf97-9aad1f944f76","resolution":{"observed_at":"2026-08-14T12:55:03.392687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.371778Z","title":"Grammatical evolution,","venue":null,"work_id":"54ed21a4-240c-4da3-9fa5-933d496f6496","year":2001},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.137250Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:0a0a2144170542d8b52f9a0c1b1aa7ebad8cd3fcdbc47c6f84f7289ede24115f","observation_id":"27448dc9-26b6-4a39-abd7-75d14b6b2daf","resolution":{"observed_at":"2026-08-14T12:55:03.377020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.355200Z","title":"Enhancing discrimination power with genetic feature construction: A grammatical evolution approach,","venue":null,"work_id":"6cd93023-cb68-4681-8fd8-f3453dd08318","year":2016},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.141645Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:a7a5a81cde902fd58bc219d4317eed0337352cd79abac69091f19d293cdab5f4","observation_id":"9102ebb3-4597-4a57-ac6e-5ed1be5ec0e3","resolution":{"observed_at":"2026-08-14T12:55:03.360371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.338858Z","title":"MBCGP-FE: A modified balanced cartesian genetic programming feature extractor,","venue":null,"work_id":"5c2706c8-02c1-4ca4-8d4e-c2bb5e18ee8c","year":2017},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.146152Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:4dfca985489a9e0a49182422739e9f09870378ee56042cd44f580f6ced205b26","observation_id":"41398daa-1d18-4604-96f7-9f0272670ef1","resolution":{"observed_at":"2026-08-14T12:55:03.344165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.323345Z","title":"Distilling Free-Form Natural Laws from Experimental Data,","venue":null,"work_id":"525010f3-fa91-49eb-bed9-85506b0d628c","year":2009},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.150613Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:b13f74902b4154e7a6fa370d71dde7a4c80f487468a190c0503bb39e99fffbed","observation_id":"eb961557-3e4d-4375-8f92-eab0975f6400","resolution":{"observed_at":"2026-08-14T12:55:03.328325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.307339Z","title":"A Survey of Probabilistic Model Building Genetic Programming,","venue":null,"work_id":"395209bb-a181-4be9-9a51-d810a9ebb432","year":2006},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.155329Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:6543c8a009f4b9ca07875f09365c1911badd22b7c8dd74bbe95314e4d6ac5daa","observation_id":"74077710-e937-4a9a-a4a8-b1f0d8c683ee","resolution":{"observed_at":"2026-08-14T12:55:03.312449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"cs/0602022","last_updated":"2006-02-07T07:48:27Z","snapshot_observed_at":"2026-07-07T03:07:30.635371Z","submitted_at":"2006-02-07T07:48:27Z","title":"Avoiding the Bloat with Stochastic Grammar-based Genetic Programming","version":1},"cited_work":{"arxiv_id":"cs/0602022","doi":null,"metadata_source":"pith","pith_arxiv_id":"cs/0602022","snapshot_observed_at":"2026-08-14T12:55:03.223153Z","title":"Avoiding the Bloat with Stochastic Grammar-based Genetic Programming","venue":"cs.AI","work_id":"f287eb89-60bb-4a73-8ae6-53031bd80f0f","year":2006},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.160032Z"},"links":{"cited_paper":"/paper/cs/0602022","citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:b25d31f695b22d607148c61f51e26982c33e214194aefce4d96e7bc9010b54aa","observation_id":"3d427746-5dce-4993-b5c8-50402adb51a3","resolution":{"observed_at":"2026-08-14T12:55:03.230486Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:55:03.291319Z","title":"Learning to discover: the Higgs boson machine learning challenge - Documentation,","venue":null,"work_id":"7810d7a8-317c-4f90-9ef8-a49fd816cc40","year":2014},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.165038Z"},"links":{"citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:d69598b1b9b3cf3d9789e5c79fea2545d8d9647d81207b7e598d18695c79523c","observation_id":"b18b952d-8147-49b3-a40c-a82ff9a6a1a9","resolution":{"observed_at":"2026-08-14T12:55:03.296593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.8548","last_updated":"2015-04-01T13:02:19Z","snapshot_observed_at":"2026-08-14T23:17:32.988828Z","submitted_at":"2014-09-30T13:51:37Z","title":"Search for the lepton flavour violating decay $\\tau^-\\to \\mu^-\\mu^+\\mu^-$","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.8548","snapshot_observed_at":"2026-08-14T12:55:03.169562Z","title":"Search for the lepton flavour violating decay τ −→ µ−µ+µ−,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T12:55:03.169562Z"},"links":{"cited_paper":"/paper/1409.8548","citing_paper":"/paper/1908.08005"},"observation_digest":"sha256:27b73bf628d9f887c12941de1d003b5f9408bfb87fa8d551cc96e9426a7e3226","observation_id":"862a3dfb-e1fc-42c5-b897-ae0f7b6565b1","resolution":{"observed_at":"2026-08-14T12:55:03.169562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.08005","last_updated":"2019-08-17T10:55:15Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-16T09:02:03.585424Z","submitted_at":"2019-08-17T10:55:15Z","title":"Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":35},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:1908.08005."}